Qwen Image 2.1 7B INT8 and INT4 (W4A8) ConvRot Quantizations, for use in ComfyUI Qwen-Image-2.1Qwen-Image-2.1, A unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. Highlights<ul><li>Efficient Image Generation: Qwen-Image-2.1 combines strong visual performance with fast inference and a compact design, making high-quality image creation accessible across a wide range of creative workflows.</li><li>Flexible Creative Control: With support for diverse inputs, outputs, and localized edits, Qwen-Image-2.1 gives creators the flexibility to explore ideas and refine details within a unified workflow.</li></ul>Four key improvements define this release:<ul><li>Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.</li><li>Native Transparency, Unified Creation and Editing — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model.</li><li>Versatile Editing — Support up to 10 reference images, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products.</li><li>Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.</li></ul>

Qwen Image 2.1 INT8/INT4

qwenImage21INT8INT4_int8_txt.safetensors

qwenImage21INT8INT4_int8.safetensors

qwen_image_2.1_vae_bf16.safetensors

INT8

INT4 (W4A8)

Repost From Huggingface <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image-2.1">https://huggingface.co/Qwen/Qwen-Image-2.1</a><h2 id="introduction">Introduction</h2>We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.Four key improvements define this release:<ul><li>Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost.</li><li>Native Transparency, Unified Creation and Editing — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model.</li><li>Versatile Editing — Support up to 10 reference images, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products.</li><li>Realistic Textures and Refined Aesthetics — Improved typography, portrait lighting, and fine details for more visually compelling results.</li></ul>

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